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The Anumana Low Ejection Fraction AI-ECG Algorithm is software intended to aid in screening for Left Ventricular Ejection Fraction (LVEF) less than or equal to 40% in adults at risk for heart failure. This population includes, but is not limited to:
· patients with cardiomyopathies

• patients who are post-myocardial infarction
• · patients with aortic stenosis
• · patients with chronic atrial fibrillation
• · patients receiving pharmaceutical therapies that are cardiotoxic, and
  • postpartum women.

Anumana Low Ejection Fraction Al-ECG Algorthm is not intended to be a stand-alone diagnostic device for cardiac conditions, should not be used for monitoring of patients, and should not be used on ECGs with a paced rhythm.

A positive result may suggest the need for further clinical evaluation in order to establish a diagnosis of Left Ventricular Ejection Fraction (LVEF) less than or equal to 40%. Additionally, if the patient is at high risk for the cardiac condition, a negative result should not rule out further non-invasive evaluation.

The Anumana Low Ejection Fraction AI-ECG Algorithm should be applied jointly with clinician judgment.

The Low Ejection Fraction AI-ECG Algorithm interprets 12-lead ECG voltage times series data using an artificial intelligence-based algorithm. The device analyzes 10 seconds of a single 12lead ECG acquisition, and within seconds provides a prediction of likelihood of LVEF (ejection fraction less than or equal to 40%) to third party software. The results are displayed by the third-party software on a device such as a smartphone, tablet, or PC. The Low Ejection Fraction AI-ECG Algorithm was trained to predict Low LVEF using positive and control cohorts, and the prediction of Low LVEF in patients is generated using defined conditions and covariates. The Low Ejection Fraction AI-ECG Algorithm device is intended to address the unmet need for a point-of-care screen for LVEF less than or equal to 40% and is expected to be used by cardiologists, front-line clinicians at primary care, urgent care, and emergency care settings, where cardiac imaging may not be available or may be difficult or unreliable for clinicians to operate. Clinicians will use the Low Eiection Fraction AI-ECG Algorithm to aid in screening for LVEF less than or equal to 40% and making a decision for further cardiac evaluation.

Here's a breakdown of the acceptance criteria and the study proving the device meets those criteria, based on the provided FDA 510(k) clearance letter for the Low Ejection Fraction AI-ECG Algorithm:

Low Ejection Fraction AI-ECG Algorithm: Acceptance Criteria and Performance Study

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size for Test Set: The clinical validation study included 16,000 patient records initially, though 2,040 records were excluded due to quality checks, resulting in a final analysis sample of 13,960 patient-ECG pairs.
• Data Provenance: The data was retrospective, collected from 4 health systems across the United States.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Their Qualifications

The document does not specify the number of experts or their qualifications used to establish the ground truth for the clinical validation test set. The ground truth (LVEF 40%) was derived from transthoracic echocardiogram (TTE) measurements. While TTE interpretation requires expertise, the document doesn't detail the method of expert review or consensus for these TTE results themselves for the test set.

4. Adjudication Method for the Test Set

The document does not specify an adjudication method (e.g., 2+1, 3+1) for the ground truth for the test set. The ground truth was established by TTE measurements.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. The study evaluated the standalone performance of the AI algorithm against a ground truth without human readers in the loop.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was Done

Yes, a standalone performance study was done. The reported sensitivity and specificity values are for the algorithm's performance alone in detecting low LVEF.

7. The Type of Ground Truth Used

The type of ground truth used for both training and validation was objective clinical measurements from Transthoracic Echocardiogram (TTE), specifically the Left Ventricular Ejection Fraction (LVEF) measurement. An LVEF of $\le$ 40% was defined as the disease cohort, and > 40% as the control cohort.

8. The Sample Size for the Training Set

The training set for the algorithm development consisted of 93,722 patients with an ECG and TTE performed within a 2-week interval. These were split into:

• Training dataset: 50% of the 93,722 patients.
• Tuning dataset: 20% of the 93,722 patients.
• Set-aside testing dataset: 30% of the 93,722 patients (used for internal validation during development, distinct from the independent clinical validation study).

9. How the Ground Truth for the Training Set Was Established

The ground truth for the training set was established using LVEF measurements obtained from transthoracic echocardiograms (TTE). Specifically, for each patient, the LVEF measurement from the earliest TTE within a 2-week interval of an ECG was paired with the closest ECG recording. LVEF $\le$ 40% defined the disease cohort, and LVEF > 40% defined the control cohort. This data was identified from a research-use authorized clinical database from Mayo Clinic.

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The CorVista® System analyzes sensor-acquired physiological signals of patients presenting with cardiovascular symptoms (such as chest pain, dyspnea, fatigue) to indicate the likelihood of significant coronary artery disease. The analysis is presented for interpretation by healthcare providers in conjunction with their clinical judgment, the patient's signs, symptoms, and clinical history as an aid in diagnosis.

The CorVista® System is a non-invasive medical device system comprised of several hardware and software components that are designed to work together to allow a physician to evaluate the patient for the presence of cardiac disease, or cardiac disease indicators, using a static detection algorithm. The CorVista System has a modular design, where disease-specific "Add-On Modules" will integrate with a single platform, the CorVista Base System, to realize its intended use. The CorVista Base System is a combination of hardware, firmware, and software components with the functionality to acquire, transmit, store, and analyze data, and to generate a report for display in a secure web-based portal. The architecture of the CorVista Base system allows for integration with indication-specific "Add-Ons" which perform data analysis using a machine learned detection algorithm to indicate the likelihood of specific diseases at point of care. The CAD Add-On indicates the likelihood of significant Coronary Artery Disease (CAD). The analysis is presented for interpretation by healthcare providers in conjunction with their clinical judgment, the patient's signs, symptoms, and clinical history as an aid in diagnosis.

Here's a breakdown of the acceptance criteria and the study proving the CorVista® System meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The text does not explicitly state pre-defined acceptance criteria in a quantitative format (e.g., "Sensitivity >= X%"). Instead, it presents the device's performance results and implies that these results were deemed acceptable for substantial equivalence to the predicate device. The comparison to CCTA's "rule out performance" suggests a benchmark, but not a strict acceptance criterion.

2. Sample Size Used for the Test Set and Data Provenance

• Test Set Sample Size: N = 1,816 subjects.
  • Population A (CAD+ for Sensitivity Testing): Number not specified, but this population was evaluated for sensitivity.
  • Population B (CAD- for Specificity Testing): Number not specified, but this population was evaluated for specificity.
• Data Provenance: Prospective, multicenter, non-randomized, repository study. The text does not explicitly state the country of origin, but given the FDA submission, it implicitly refers to data collected in the US.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications of Experts

The text states that the ground truth for CAD was established via "invasive catheterization (ICA)" or "core-lab adjudicated CTA."

• Number of Experts: Not explicitly stated for ICA or CTA adjudication.
• Qualifications of Experts: It implies that medical professionals performed the ICA, and a core-lab performed the CTA adjudication. The specific qualifications (e.g., number of years of experience for radiologists or cardiologists performing these procedures/adjudications) are not detailed.

4. Adjudication Method for the Test Set

The adjudication method for the ground truth was:

• For ICA: Clinical outcome from invasive coronary angiography. This is a direct, invasive diagnostic procedure.
• For CTA: "Core-lab adjudicated CTA." This implies a standardized process by a specialized lab, likely involving multiple readers or a defined quality control process, but the specific multi-reader method (e.g., 2+1, 3+1) is not provided.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

• No, an MRMC comparative effectiveness study was not explicitly stated to have been done for human readers with and without AI assistance to assess improvement. The study described focuses on the standalone performance of the CorVista System compared to established diagnostic methods (ICA/CTA). The device is intended to be an "aid in diagnosis" used "in conjunction with their clinical judgment," but the study design presented does not evaluate the human-AI interaction in a comparative effectiveness study setting.

6. If a Standalone (algorithm only without human-in-the-loop performance) was done

• Yes, a standalone performance study was done. The described clinical testing focuses on the algorithm's performance in indicating the likelihood of significant CAD by comparing its predictions to objective ground truth (ICA/CTA results). The reported sensitivity, specificity, and AUC-ROC are measures of the algorithm's standalone performance.

7. The Type of Ground Truth Used

• Objective Clinical Data / Outcomes Data: The ground truth for the test set was established by:
  • Invasive Coronary Angiography (ICA): This is considered a gold standard for diagnosing CAD.
  • Core-lab Adjudicated Coronary Computed Tomography Angiography (CTA): This is another strong diagnostic imaging modality, with the "core-lab adjudicated" aspect indicating a high level of rigor in interpretation.
    These methods directly determine the patient's actual CAD classification (CAD+ or CAD-).

8. The Sample Size for the Training Set

• The text states the ground truth for the "Model Training and Validation" was "Guideline-driven ground truth via invasive catheterization or core-lab adjudicated CTA." However, the specific sample size for the training set is not provided. The N=1,816 refers to the validation population (test set) used for performance testing.

9. How the Ground Truth for the Training Set Was Established

• The ground truth for model training (and validation) was established using "Guideline-driven ground truth via invasive catheterization or core-lab adjudicated CTA." This implies that the same rigorous, objective diagnostic methods used for the test set's ground truth were also used to label the data utilized during the training and internal validation phases of the algorithm development.

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